Masahiro Ono

ICRA Conference 2025 Conference Paper

Risk-Aware Integrated Task and Motion Planning for Versatile Snake Robots Under Localization Failures

• Ashkan Jasour
• Guglielmo Daddi
• Masafumi Endo
• Tiago Vaquero
• Michael Paton
• Marlin P. Strub
• Sabrina Corpino
• Michel D. Ingham

Snake robots enable mobility through extreme terrains and confined environments in terrestrial and space applications. However, robust perception and localization for snake robots remain an open challenge due to the proximity of the sensor payload to the ground coupled with a limited field of view. To address this issue, we propose Blind-motion with Intermittently Scheduled Scans (BLISS) which combines proprioception-only mobility with intermittent scans to be resilient against both localization failures and collision risks. BLISS is formulated as an integrated task and motion planning (TAMP) problem that leads to a chance-constrained hybrid partially observable Markov decision process (CC-HPOMDP), known to be computationally intractable due to the curse of history. Our novelty lies in reformulating CC-HPOMDP as a tractable, convex mixed integer linear program. This allows us to solve BLISS-TAMP significantly faster and jointly derive optimal task-motion plans. Simulations and hardware experiments on the EELS snake robot show our method achieves over an order of magnitude computational improvement compared to state-of-the-art POMDP planners and $>50 \%$ better navigation time optimality versus classical two-stage planners.

IROS Conference 2023 Conference Paper

EELS: Towards Autonomous Mobility in Extreme Terrain with a Versatile Snake Robot with Resilience to Exteroception Failures

• Rohan Thakker
• Michael Paton
• Marlin P. Strub
• R. Michael Swan
• Guglielmo Daddi
• Rob Royce
• L. Phillipe Tosi
• Matthew Gildner

The discovery of ocean worlds such as Enceladus, Titan, and Europa motivates the development of versatile autonomous mobility systems to enable the next era of space exploration where there is large uncertainty in terrain specifications due to a lack of prior surface reconnaissance missions. To explore these environments, we propose Exobiology Extant Life Surveyor (EELS): the first large-scale (4 lm long with 400 Nm peak torque) snake robot. The large scale is achieved by using a screw-based active skin mechanism to decouple motion and shape control. Autonomous mobility for such a system remains an open problem due to its many Degrees of Freedom (DoFs), complex terrain interactions, and intermittent localization failures in GPS-denied perceptually degraded environments due to the presence of fog, dust, featureless terrains, etc. We propose NEO, an autonomy architecture that scales to large DoFs to generate a versatile set of gaits to achieve mobility in unknown extreme environments. We also discuss the resilience capabilities of NEO that achieves closed-loop tracking performance by leveraging exteroception when available but can also operate with proprioception only, leading to resiliency against localization failures via graceful degradation in performance rather than unsafe behaviors. A quantitative hardware evaluation of exteroceptive leader-follower gait is performed indoors on synthetic ice along with qualitative results of field deployment of the proprioceptive leader-follower and sidewinding gaits in extreme environments of icy and sandy terrains with mobility-stressing elements such as trenches, undulations, and steep slopes (up to 35 degrees). We present a set of lessons learned from field deployments with a summary of challenges and open research problems. Video: www. rohanthakker. in/eels-neo-autonomy. html

IROS Conference 2023 Conference Paper

Principled ICP Covariance Modelling in Perceptually Degraded Environments for the EELS Mission Concept

• William Talbot
• Jeremy Nash
• Michael Paton
• Eric Ambrose
• Brandon Metz
• Rohan Thakker
• Rachel Etheredge
• Masahiro Ono

The Exobiology Extant Life Surveyor (EELS) is a snake-like mobile instruments platform under development at Jet Propulsion Laboratory (JPL) for a mission concept to find evidence of life on Saturn's sixth largest moon, Enceladus. To conduct a life surveying mission there, the EELS platform must first traverse an unknown icy surface terrain before undertaking a controlled descent into a cryovolcanic vent. The remoteness of Enceladus and the icy nature of its terrain demands a level of autonomy in navigation significantly higher than previous rover missions. The perception system onboard EELS must be highly resilient to perceptually-degraded environments such as flat, open ice fields, icy plumes, and repeating geometries in vents. EELS' perception system is implemented as a multi-sensor Simultaneous Localisation And Mapping (SLAM) solution called SERPENT. State Estimation through Robust Perception in Extreme and Novel Terrains (SERPENT) estimates the robot trajectory and maintains a map database, from which dense global or local maps can be obtained on demand for downstream planning algorithms. This system opts to incorporate measurements from many sensor modalities (laser scans, images, IMU, altimeter, etc.), solving the SLAM problem through joint optimisation, and thus requires that the contribution of each sensor be balanced through careful modelling of their uncertainties. With a specific focus on Light Detection And Ranging (LiDAR) in this context, this paper proposes a principled approach to model the covariances of point-to-plane Iterative Closest Point (ICP). It performs a rigorous comparative analysis of new and existing covariance models, and is the first time some of these have been tested within a complete SLAM pipeline. These models are evaluated on perceptually challenging datasets collected in glacial environments by the EELS sensor suite (see Figures 1, 2). SERPENT is open-sourced at https://github.com/jpl-eels/serpent.

UAI Conference 2019 Conference Paper

Co-training for Policy Learning

• Jialin Song
• Ravi Lanka
• Yisong Yue
• Masahiro Ono

We study the problem of learning sequential decision-making policies in settings with multiple state-action representations. Such settings naturally arise in many domains, such as planning (e. g. , multiple integer programming formulations) and various combinatorial optimization problems (e. g. , those with both integer programming and graph-based formulations). Inspired by the classical co-training framework for classification, we study the problem of co-training for policy learning. We present sufficient conditions under which learning from two views can improve upon learning from a single view alone. Motivated by these theoretical insights, we present a meta-algorithm for co-training for sequential decision making. Our framework is compatible with both reinforcement learning and imitation learning. We validate the effectiveness of our approach across a wide range of tasks, including discrete/continuous control and combinatorial optimization.

YNICL Journal 2019 Journal Article

Misery perfusion and amyloid deposition in atherosclerotic major cerebral artery disease

• Hiroshi Yamauchi
• Shinya Kagawa
• Masaaki Takahashi
• Naoya Oishi
• Masahiro Ono
• Tatsuya Higashi

IROS Conference 2018 Conference Paper

Probabilistic Kinematic State Estimation for Motion Planning of Planetary Rovers

• Sourish Ghosh
• Kyohei Otsu
• Masahiro Ono

Kinematics-based collision detection is important for robot motion planning in unstructured terrain. Especially, planetary rovers require such capability as a single collision may lead to the termination of a mission. For onboard computation, typical numeric approaches are unsuitable as they are computationally expensive and unstable on rocky terrain; instead, a light-weight analytic solution (ACE: Approximate Clearance Evaluation) is planning to be used for the Mars 2020 rover mission. ACE computes the state bounds of articulated suspension systems from terrain height bounds, and assess the safety by checking the constraint violation of states with the worst-case values. ACE's conservative safety check approach can sometimes lead to over-pessimism: feasible states are often reported as infeasible, thus resulting in frequent false positive detection. In this paper, we introduce a computationally efficient probabilistic variant of ACE (called p-ACE) which estimates the probability distributions of states in real time. The advantage of having probability distributions over states, instead of deterministic bounds, is to provide more flexible and less pessimistic worst-case evaluation with probabilistic safety guarantees. Empirically derived distribution models are used to compute the total probability of constraint satisfaction, which is then used for path assessment. Through experiments with a high-fidelity simulator, we empirically show that p-ACE relaxes the deterministic state bounds without losing safety guarantees.

AAAI Conference 2018 Conference Paper

Safe Exploration and Optimization of Constrained MDPs Using Gaussian Processes

• Akifumi Wachi
• Yanan Sui
• Yisong Yue
• Masahiro Ono

We present a reinforcement learning approach to explore and optimize a safety-constrained Markov Decision Process (MDP). In this setting, the agent must maximize discounted cumulative reward while constraining the probability of entering unsafe states, defined using a safety function being within some tolerance. The safety values of all states are not known a priori, and we probabilistically model them via a Gaussian Process (GP) prior. As such, properly behaving in such an environment requires balancing a three-way trade-off of exploring the safety function, exploring the reward function, and exploiting acquired knowledge to maximize reward. We propose a novel approach to balance this trade-off. Specifically, our approach explores unvisited states selectively; that is, it prioritizes the exploration of a state if visiting that state significantly improves the knowledge on the achievable cumulative reward. Our approach relies on a novel information gain criterion based on Gaussian Process representations of the reward and safety functions. We demonstrate the effectiveness of our approach on a range of experiments, including a simulation using the real Martian terrain data.

ICRA Conference 2017 Conference Paper

Locally-adaptive slip prediction for planetary rovers using Gaussian processes

• Christopher Cunningham
• Masahiro Ono
• Issa A. D. Nesnas
• Jeng Yen
• William Whittaker

This paper presents a method for predicting slip using Gaussian process regression. Slip models are learned for visually classified terrain types as a function of terrain geometry. Spatial correlations between terrain properties are leveraged for on-line slip model adaptation. Results show that regression-based modeling using in-situ rover data outperforms the state-of-practice, terrestrially-calibrated slip curves in both mean prediction and uncertainty bounds. Local adaptation improves slip prediction results, particularly in high-slip sand areas that pose the greatest threat to rovers. Slip estimates made using a visual classifier to identify terrain type are compared to estimates using on-line model selection with only proprioceptive slip measurements as inputs. The proprioceptive results nearly match the visual results, showing that this approach could work even when a visual classifier is not available.

ICAPS Conference 2013 Conference Paper

Paper Summary: Probabilistic Planning for Continuous Dynamic Systems under Bounded Risk

• Masahiro Ono
• Brian Williams 0001
• Lars Blackmore

This paper presents a model-based planner called the Probabilistic Sulu Planner or the p-Sulu Planner, which controls stochastic systems in a goal directed manner within user-specified risk bounds. We first develop a new plan representation called a chance-constrained qualitative state plan (CCQSP), through which users can specify the desired evolution of the plant state as well as the acceptable level of risk. We then develop the p-Sulu Planner, which can tractably solve a CCQSP planning problem.

AAMAS Conference 2010 Conference Paper

Market-based Risk Allocation for Multi-agent Systems

• Masahiro Ono
• Brian Williams

This paper proposes Market-based Iterative Risk Allocation (MIRA), a new market-based decentralized optimization algorithm for multi-agent systems under stochastic uncertainty, with a focus on problems with continuous action and state space. In large coordinationproblems, from power grid management to multi-vehicle missions, multiple agents act collectively in order to maximize the performance of the system, while satisfying mission constraints. Theseoptimal action plans are particularly susceptible to risk when uncertainty is introduced. We present a decentralized optimization algorithm that minimizes the system cost while ensuring that the probability of violating mission constraints is below a user-specified upper bound. We build upon the paradigm of risk allocation, in which theplanner optimizes not only the sequence of actions, but also its allocation of risk among state constraints. We extend the concept ofrisk allocation to multi-agent systems by highlighting risk as a resource that is traded in a computational market. The equilibriumprice of risk that balances the supply and demand is found by aniterative price adjustment process called t\^{a}tonnement (also knownas Walrasian auction). Our work is distinct from the classical t\^{a}tonnement approach in that we use Brent's method to provide fastguaranteed convergence to the equilibrium price. The simulationresults demonstrate the efficiency and optimality of the proposeddecentralized optimization algorithm.

AAAI Conference 2008 Conference Paper

An Efficient Motion Planning Algorithm for Stochastic Dynamic Systems with Constraints on Probability of Failure

• Masahiro Ono

When controlling dynamic systems, such as mobile robots in uncertain environments, there is a trade off between risk and reward. For example, a race car can turn a corner faster by taking a more challenging path. This paper proposes a new approach to planning a control sequence with a guaranteed risk bound. Given a stochastic dynamic model, the problem is to find a control sequence that optimizes a performance metric, while satisfying chance constraints i. e. constraints on the upper bound of the probability of failure. We propose a two-stage optimization approach, with the upper stage optimizing the risk allocation and the lower stage calculating the optimal control sequence that maximizes reward. In general, the upper-stage is a non-convex optimization problem, which is hard to solve. We develop a new iterative algorithm for this stage that efficiently computes the risk allocation with a small penalty to optimality. The algorithm is implemented and tested on the autonomous underwater vehicle (AUV) depth planning problem, and demonstrates a substantial improvement in computation cost and suboptimality, compared to the prior arts.

AAAI Conference 2008 Short Paper

Distributed Robust Execution of Qualitative State Plan with Chance Constraints

• Masahiro Ono

ICRA Conference 2008 Conference Paper

Experimental validation of a fuel-efficient robotic maneuver control algorithm for very large flexible space structures

• Masahiro Ono
• Peggy Boning
• Tatsuro Nohara
• Steven Dubowsky

The robotic maneuvering of large space structures is key to a number of future orbital missions. In this paper a large space structure maneuver control algorithm, recently proposed, is extended and experimentally validated. The method uses space robots’ manipulators to control the vibration of the structures being maneuvered and their reaction jets perform the large motion maneuvers. The algorithm quickly damps out the vibrations and requires less fuel than reaction jet-based vibration control methods. The approach is called maneuver decoupled control. Its performance is demonstrated and quantitatively evaluated in simulation and experiments.